Sparse sensor selection for distributed systems: An l1-relaxation approach

We study the problem of sensor selection for distributed systems, where a large number of sensors are located spatially in many different locations. Specifically, we consider both perfect and packet-dropping communication channels. While the original problem is NP-hard, by adopting a sparse design,...

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Bibliographic Details
Published inAutomatica (Oxford) Vol. 165
Main Authors Zhong, Yuxing, Yang, Nachuan, Huang, Lingying, Shi, Guodong, Shi, Ling
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.07.2024
Subjects
[formula omitted]-relaxation
Convex optimization
Packet dropouts
Sensor selection
Sparsity
l1-relaxation
Sparsity
Convex optimization
Packet dropouts
Sensor selection
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Summary:We study the problem of sensor selection for distributed systems, where a large number of sensors are located spatially in many different locations. Specifically, we consider both perfect and packet-dropping communication channels. While the original problem is NP-hard, by adopting a sparse design, we can solve the problem via convex optimization and reduce the computation cost significantly. Our method not only handles correlated measurement noise but also can be easily extended to actuator selection or sensor-and-actuator (SaA) selection problems. Simulation shows that our sparsity-based approach performs similarly to the brute force optimal strategy while consuming significantly less computation time. Additionally, our method is shown to outperform the state-of-art method notably.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2024.111670